Overview of performance values

The following statistics were calculated from the performance values of each algorithm:
obs nas min qu_1st med mean qu_3rd max sd coeff_var
standard 2024 0 0.022996 0.205718 0.799379 798.394 45.785 5000 1767.67 2.21404
learning 2024 0 0.026995 0.219967 0.567414 1433.08 4916.26 5000 2174.91 1.51765

Summary of the runstatus per algorithm

The following table summarizes the runstatus of each algorithm over all instances (in %).

ok timeout memout not_applicable crash other
learning 75.099 24.901 0.000 0.000 0.000 0.000
standard 85.771 14.229 0.000 0.000 0.000 0.000

Dominated Algorithms

Here, you'll find an overview of dominating/dominated algorithms:
None of the algorithms was superior to any of the other.

An algorithm (A) is considered to be superior to an other algorithm (B), if it has at least an equal performance on all instances (compared to B) and if it is better on at least one of them. A missing value is automatically a worse performance. However, instances which could not be solved by either one of the algorithms, were not considered for the dominance relation.


Important note w.r.t. some of the following plots:
If appropriate, we imputed performance values for failed runs. We used max + 0.3 * (max - min), in case of minimization problems, or min - 0.3 * (max - min), in case of maximization problems.
In addition, a small noise is added to the imputed values (except for the cluster matrix, based on correlations, which is shown at the end of this page).

Boxplots of performance values


Performance values with imputation.
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Estimated densitities of performance values


Performance values with imputation.
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Performance values without imputation.
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Estimated cumulative distribution functions of performance values


Performance values without imputation.
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Scatterplot matrix of the performance values

The figure underneath shows pairwise scatterplots of the performance values.

Performance values with imputation.
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Clustering algorithms based on their correlations

The following figure shows the correlations of the ranks of the performance values. Per default it will show the correlation coefficient of spearman. Missing values were imputed prior to computing the correlation coefficients. The algorithms are ordered in a way that similar (highly correlated) algorithms are close to each other. Per default the clustering is based on hierarchical clustering, using Ward's method.

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